Medical decision process has been traditionally considered unstructured and ill-posed. Indeed, the ill-posed nature of medical diagnosis and decision making has given rise to the perception that medical diagnosis is a form of art, which cannot be quantified or structured. The main difficulties of medical decision making are related to the following key issues.
First, the nature of medical information processing is inherently probabilistic with a large number of possible diseases, disease stages, side-effects, complications, etc. These possibilities can be alternative, additive, complementary, correlated, partially correlated, or uncorrelated. For example, a pain in the chest area can be caused by heart disease, stomach ulcer, back problems, hypochondriac, neurological conditions, or various combinations of these disorders. In addition, a person might have a combination of different diseases that are not related to the symptoms being investigated but, nevertheless, might change the patient's symptoms and obscure diagnosis. For example, a combination of heart angina and back pain might be difficult to differentiate, because both diseases might have similar symptoms.
Second, there is enormous individual variability in the expression of diseases, which creates completely different profiles of the same illness in different subjects. For example, myocardial infarction (heart attack) can be manifested by pain in the upper left area of the chest, the central region of the chest, left arm, back, or shortness of breath.
Third, incompleteness of information represents a significant problem in medical decision making. In particular, information about different diagnostic tests performed at different times can be distributed among different databases located in different medical institutions. For example, a surgical procedure performed two years ago can be located in that hospital's database, whereas subsequent tests were performed in a different hospital and are located in that hospital's database. Some of the local databases distributed among different medical institutions may be temporarily or permanently unavailable. Thus, a mechanism is needed to estimate the total information completeness, and this information completeness needs to be tracked dynamically, as new information becomes available over time.
Due to these reasons, the “art” of medical diagnosis has traditionally been considered as an ability to weight all probable causes of illness in the shortest possible time in order to start an appropriate treatment as early as possible.
Recent developments of computer and network technologies have created a technological background for incorporation of the ill-posed medical decision making rules and facts into computer and network algorithms. A number of studies have examined this problem, using statistical analysis, pattern recognition, neural networks, and expert systems. For example, application of methods of artificial intelligence for medical diagnosis have been described by Shusterman et al. in Building an application of Expert Systems For Differential Diagnostics of Cardiovascular Diseases, SAMS, 1994, Vol. 14, pp. 15-24, Yan et al. in The Internet-based Knowledge Acquisition and Management Method to Construct Large-scale Distributed Medical Expert Systems, Comput Methods Programs Biomed. 2004 April; 74(1): 1-10, and Baxt et al. in A Neural Network Aid for the Early Diagnosis of Cardiac Ischemia in Patients Presenting to the Emergency Department with Chest Pain, Annals of Emergency Medicine, December 2002 40:06, among other publications.
Various techniques for computerized identification and analysis of health data are also described in several United States patents. For example, Barnhill et al. in the U.S. Pat. No. 6,882,990, (2005) discloses methods of identifying biological patterns using multiple data sets. Using learning process on the training data, optimal solutions are determined for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Bardy in the U.S. Pat. No. 6,887,201 (2005) describes system and method for determining a reference baseline of regularly retrieved patient information for automated remote patient care. The method uses a database of patient records to determine a set of reference measures. Asada et al. in U.S. Pat. No. 5,463,548 (1995) disclose a method and system for differential diagnosis based on clinical and radiological information using artificial neural networks. The method uses radiographic data and clinical information to differentiate mammographic images and lung diseases. Leatherman in the U.S. Pat. No. 5,544,044 (1996) discloses a method for evaluation of health care quality using analysis of health care claims records to assess the quality of care based on conformance to nationally recognized medical practice guidelines or quality indicators and to provide a means to supplement claims with data from patient medical records. Iliff in the U.S. Pat. Nos. 5,594,638 (1997), 5,868,669 (1999), 6,113,540 (2000), 6,206,829 (2001), 6,482,156 (2002), and 6,849,045 (2005) disclose systems and methods for providing computerized, knowledge-based medical diagnostic and treatment advice. “Meta” functions for pattern matching and time-density analysis are included to determine the similarity and the number of medical complaints per unit of time. A re-enter feature monitors the user's changing condition over time. A symptom severity analysis helps to respond to the changing conditions. System sensitivity factors may be changed to adjust the system advice as necessary. Zimmerman in the U.S. Pat. No. 5,941,820 (1999) discloses a method for measuring patient data, determining statistics from the data, variation within the data, homeostasis, modifying control chart limits based on the measure of homeostasis and displaying the statistic on the modified control chart. The control charts are modified as data varies over time. By determining the amount of consistency or similarity using autocorrelation or serial correlation, significant changes are identified. Herren et al. in the U.S. Pat. No. 6,108,635 (2000) discloses a system for drug discovery, design of clinical trials, performing pharmacoeconomic analysis, and illustrating disease progression over time. Freedman in the U.S. Pat. No. 6,126,596 (2000) discloses a system for collecting data and using these data for diagnosis and lookup of appropriate treatments. Barry et al. in the U.S. Pat. No. 6,188,988 (2001) disclose systems, methods and computer program products for guiding the selection of treatment, which comprise (a) providing patient information to a computing device (a knowledge base and expert rules for selecting treatment and advisory information; (b) generating a listing of treatments; and (c) generating advisory information. Papageorge in the U.S. Pat. No. 6,584,445 (2003) discloses a computerized health evaluation system for joint patient-physician decision making. The system includes a patient input module, a physician input module, and a database of medical information about diseases. The computer system uses an algorithm for weighing the patient data and the physician data and generating a report with various treatment options. Sadeghi et al. in the U.S. Pat. No. 6,687,685 (2004) discloses a system and method for automated medical decision-making, such as online, questionnaire-based medical triage. Information is modeled in a Bayesian Network, and the conditional probability may be determined in a real-time.